[1]梁晔,于剑.面向社群图像的显著区域检测方法[J].智能系统学报,2018,13(02):174-181.[doi:10.11992/tis.201706043]
 LIANG Ye,YU Jian.Salient region detection for social images[J].CAAI Transactions on Intelligent Systems,2018,13(02):174-181.[doi:10.11992/tis.201706043]
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面向社群图像的显著区域检测方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年02期
页码:
174-181
栏目:
学术论文—智能系统
出版日期:
2018-04-15

文章信息/Info

Title:
Salient region detection for social images
作者:
梁晔12 于剑2
1. 北京联合大学 机器人学院, 北京 100101;
2. 北京交通大学 计算机与信息技术学院, 北京 100044
Author(s):
LIANG Ye12 YU Jian2
1. College of Robotics, Beijing Union University, Beijing 100101, China;
2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
关键词:
显著性显著区域社群图像深度学习标签
Keywords:
saliencysalient regionsocial imagesdeep learningtag
分类号:
TP311
DOI:
10.11992/tis.201706043
摘要:
网络技术和社交网站的发展带来了社群图像的飞速增长。海量的社群图像成为了非常重要的图像类型。本文关注社群图像的显著区域检测问题,提出基于深度特征的显著区域检测方法。针对社群图像带有标签的特点,在系统框架中,本文采取两条提取线:基于CNN特征的显著性计算和基于标签的语义计算,二者的结果进行融合。最后,通过全连接的条件随机场模型对融合的显著图进行空间一致性优化。此外,为了验证面向社群图像的显著区域检测方法的性能,针对目前没有面向社群图像的带有标签信息的显著性数据集,基于NUS-WIDE数据集,本文构建了一个图像结构丰富的社群图像数据集。大量的实验证明了所提方法的有效性。
Abstract:
The development of network technology and social website has brought about the rapid growth of social images. Massive social images have become a very important image type. This paper focuses on the detection problem of salient region for social images, a method for detecting salient region and based on depth features was proposed. By considering the feature that the social image is attached with tag, in the framework of the system, the paper used two extraction lines: the saliency computing based on CNN features and the semantic computing based on tag, the results of both parts were fused. Finally, saliency maps were optimized by a fully connected conditional random field model for the spatial consistency. In addition, for verifying the performances of the saliency region detection method orienting social image, in view of the lack of saliency dataset with tags for social images, on basis of NUS-WIDE dataset, the paper constructed a social image dataset with rich image structures. Extensive experiments demonstrated the effectiveness of the proposed method.

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备注/Memo

备注/Memo:
收稿日期:2017-06-11。
基金项目:北京市自然科学基金项目(4182022);北京联合大学2017年度人才强校百杰计划项目(BPHR2017CZ10);“十三五”时期北京市属高校高水平教师队伍建设支持计划项目(IDHT20170511);国家科技支撑计划项目(2015BAH55F03)
作者简介:梁晔,1978年生,女,讲师,主要研究方向为图像处理和机器学习,发表中文核心和三大检索论文10余篇;于剑,男,1969年生,教授,博士生导师,博士,主要研究方向为2005年分别获得第八届北京青年优秀科技论文奖一等奖、第七届詹天佑铁道科技奖北京交通大学专项基金奖,2006年获得霍英东青年教师基金,并入选教育部新世纪优秀人才支持计划。发表学术论文30余篇。
通讯作者:梁晔.E-mail:liangye@buu.edu.cn.
更新日期/Last Update: 1900-01-01